Home – Home – Jornals – Avtometriya 2020 number 6

This study is aimed at a comparative analysis of the developed classical machine learning models based on linear models and decision trees, as well as modern algorithms of convolutional neural networks and a neural network autoencoder for solving the problem of predictive detection of pre-failure and emergency conditions of aircraft engines. The simulations are performed using a NASA dataset based on sensor data from aircraft engine life cycles. Several formulations of problems are considered: problems of binary and multi-class classification of normal, pre-failure, and emergency states of aircraft engines, a regression problem for predicting the exact number of operating cycles before the engine failure, and an unsupervised learning problem in which a neural network autoencoder is used to detect abnormal operating cycles of an aircraft engine. The resulting algorithms are combined into a programming framework, which can be useful for analyzing a wide range of predictive maintenance data.